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OpenAI built the Sora Android app (which hit #1 app in the world) in just 18 days with the help of Codex
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How to build your PM second brain with ChatGPT Step 1: Create its personality. Create a ChatGPT Project, and give it instructions that describe what it's job is. Use ChatGPT to help you craft those instructions. Here's a prompt you can use: I’m a PM building an AI agent. I’m building a ChatGPT Project to be my thought partner, something that’ll work with me on my initiatives, something that’ll know how to challenge me in all the right places, push back on areas that feel weak, and creatively think of alternatives with me. This Project’s “personality” has to be sharp, smart, fun, and not always agreeing with everything I come up with. It also needs to be a pro at product management—this includes product sense and product execution, with a strong sense for product taste and delight. Can you help me write the instructions for this project? :) Cheers! Step 2: Feed it information. Go to “Add files” and dump all of your PRDs, docs, decks, Excel/CSV sheets, and your key dashboards, website pages, and Slack channels (exported as PDFs). Step 3: Let it cook. Once it holds your context, talk to it about everything you've got on your plate: ideating on onboarding optimizations, developing prototypes, improving emails and decks, revisiting a strategy doc, prioritizing your roadmap...and so on. More here:
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"I was drowning." That's how Amir Klein (@amir_product) describes his first month as a PM at https://t.co/5q0a0CJk8N, tasked with building their first AI agent. Context about the project lived everywhere: Slack channels, Notion pages, Monday boards, decks, Google Docs. Hundreds of fragments of context he couldn't keep straight. Instead of doing what most of us do—trying to hold it all in his head—he tried something different. He dumped everything into a ChatGPT Project. Word vomited everything on his mind. Even asked it for help on how to get started. "Finally, I felt like I could smell a roadmap on the horizon, a direction was forming, and things began to click." In this week's newsletter, Amir shares the exact system he used to build his PM second brain with ChatGPT—and how it helped him go from overwhelmed to shipping a major AI product launch. (The workflow works in Claude and Gemini too, he shows how to set it up in all three. Check it out:
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RT @brettberson: After nearly 8 years, Angel Track has become the place where serious angel investors come to sharpen their craft. We don’t…
Deeply researched product, growth, and career advice
My biggest takeaways from @embirico (OpenAI Codex Product Lead): 1. OpenAI’s initial Codex product was “too far in the future.” It ran in the cloud asynchronously, which was great for power users but hard for newcomers. Growth exploded when they brought it back to where engineers already work: inside their code editor, on their own computer. Codex usage has grown 20x in the past 6 months. 2. OpenAI built the Sora Android app—which hit #1 in the app store—in just a few weeks with two or three engineers, with the help of Codex. The Sora app went from zero to employee testing in 18 days, then launched publicly 10 days later. Codex helped by analyzing the existing iOS app, generating work plans, and implementing features by comparing both platforms simultaneously. 3. The key to getting value from Codex: give it your hardest problems, not your easiest. These tools are built to tackle gnarly bugs and complex tasks, not simple ones. Start with something you’d otherwise spend hours on. 4. Writing code may become the universal way AI accomplishes any task. Rather than clicking through interfaces or building separate integrations, AI performs best when it writes small programs on the fly. This suggests that coding ability should be built into every AI assistant, not just specialized programming tools. 5. Designers at OpenAI now write and ship their own code. The design team maintains a fully functional prototype built with AI assistance. When they have an idea, they code it directly, test it, and often submit it for production themselves. Engineers only step in when the codebase is particularly complex. 6. Even if AI models stopped improving tomorrow, there are still years of product work left to unlock their potential. The technology is ahead of our ability to use it optimally. 7. The biggest bottleneck to AI productivity isn’t the AI; it’s how fast humans can type. The limiting factors are how fast you can type prompts and how quickly you can review AI-generated work. Until AI can validate its own output more reliably and surface help proactively, we won’t see the full productivity gains these tools could deliver. 8. Writing code is becoming less fun than reviewing AI-written code. Engineers love the creative flow of building. Now they’re spending more time reading what the AI produced. The next challenge is making that review process faster and more satisfying. 9. New AI models can now work continuously for 24 to over 60 hours on a single task. A technique called “compaction” lets the AI summarize what it’s learned before running out of memory, then continue working in a fresh session. This enables overnight or multi-day autonomous work that wasn’t previously possible. 10. If you’re starting a company today, deep understanding of a specific customer matters more than being good at building. Building is getting easier. Knowing what to build—and for whom—is the real advantage now.
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RT @nishan3000: One of the best episodes of any podcast. Makes sense that this is the guy behind Codex.
Deeply researched product, growth, and career advice